An Adaptive Deep Learning Architecture Integrating Local Feature Enrichment, Adaptive Learning Rate, and Channel Recalibration for Acute Lymphoblastic Leukemia Image Classification

This study presents a comprehensive investigation of leukemic B-lymphoblast cell classification using a model-based deep learning pipeline, enhanced with Local Feature Enrichment (LFE), Adaptive Learning Rates (ALR), and Adaptive Channel Recalibration (ACR).Six experimental scenarios were evaluated to analyze the impact of each adaptive mechanism on model performance.Scenarios 1 to 3 employed fixed learning rates without LFE or ACR, yielding moderate performance with accuracy ranging from 92.13% to 93.47%, reflecting limitations in adapting to subtle morphological variations.Scenarios 4 to 6 incorporated adaptive techniques that progressively improved performance, with Scenario 6 achieving peak metrics: accuracy of 99.94%, precision of 99.91%, recall of 99.89%, F1score of 99.90%, and specificity of 99.87%.Comparative evaluation against state-of-the-art methods, including CNNbased ECA modules (91.1%),Vision Transformer (88.2%),Majority Voting Technique (98.5%),EfficientNetB3 (99.31%), and Hybrid Encapsulation EfficientNetV2-S (99.92%), demonstrated that the proposed method outperforms previous approaches, providing superior accuracy, robustness, and stability.External validation using the ALL-IDB2 dataset confirmed that the model maintains high performance across all six scenarios, with accuracy ranging from 90.83% to 97.48%, supporting the generalizability and reliability of the proposed framework.These results confirm that the synergistic integration of adaptive learning strategies and feature enrichment significantly enhances classification performance, offering a reliable framework for automated leukemia detection from blood smear images.The high performance and reliability of this approach can contribute considerably to Public Health by enabling faster, more accurate disease screening at the population level.They may serve as a foundation for future product innovation in diagnostic technologies.